The Fornax Deep Survey (FDS) with the VST
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Astronomy & Astrophysics manuscript no. aanda ©ESO 2021
January 15, 2021
The Fornax Deep Survey (FDS) with the VST
XI. The search for signs of preprocessing between the Fornax main cluster and
Fornax A group
A. H. Su1 , H. Salo1 , J. Janz1, 2 , E. Laurikainen1 , A. Venhola1 , R. F. Peletier3 , E. Iodice4 , M. Hilker5 , M. Cantiello6 ,
N. Napolitano4 , M. Spavone4 , M. A. Raj4 , G. van de Ven7 , S. Mieske8 , M. Paolillo9 , M. Capaccioli9 , E. A. Valentijn3 ,
and A. E. Watkins10
1
Space Physics and Astronomy Research Unit, University of Oulu, Pentti Kaiteran katu 1, FI-90014, Finland
arXiv:2101.05699v1 [astro-ph.GA] 14 Jan 2021
e-mail: hung-shuo.su@oulu.fi
2
Finnish Centre of Astronomy with ESO (FINCA), University of Turku, Väisäläntie 20, FI-21500 Piikkiö, Finland
3
Kapteyn Institute, University of Groningen, Landleven 12, 9747 AD Groningen, The Netherlands
4
INAF, Osservatorio Astronomico di Capodimonte, Salita Moiariello 16,I-80131 Napoli, Italy
5
European Southern Observatory, Karl-Schwarzschild-Strasse 2, 85748 Garching bei München, Germany
6
INAF, Osservatorio Astronomico d’Abruzzo, Via Mentore Maggini Teramo,TE I-64100, Italy
7
Department of Astrophysics, University of Vienna, Türkenschanzstrasse 17, 1180 Wien, Austria
8
European Southern Observatory, Alonso de Cordova 3107, Vitacura, Santiago, Chile
9
University of Naples Federico II, C.U. Monte Sant’Angelo, Via Cinthia, 80126 Naples, Italy
10
Astrophysics Research Institute, Liverpool John Moores University, IC2, Liverpool Science Park, 146 Brownlow Hill, Liverpool
L3 5RF, UK
Received 9 October 2020 / Accepted 11 January 2021
ABSTRACT
Context. Galaxies either live in a cluster, a group, or in a field environment. In the hierarchical framework, the group environment
bridges the field to the cluster environment, as field galaxies form groups before aggregating into clusters. In principle, environmental
mechanisms, such as galaxy–galaxy interactions, can be more efficient in groups than in clusters due to lower velocity dispersion,
which lead to changes in the properties of galaxies. This change in properties for group galaxies before entering the cluster environ-
ment is known as preprocessing. Whilst cluster and field galaxies are well studied, the extent to which galaxies become preprocessed
in the group environment is unclear.
Aims. We investigate the structural properties of cluster and group galaxies by studying the Fornax main cluster and the infalling
Fornax A group, exploring the effects of galaxy preprocessing in this showcase example. Additionally, we compare the structural
complexity of Fornax galaxies to those in the Virgo cluster and in the field.
Methods. Our sample consists of 582 galaxies from the Fornax main cluster and Fornax A group. We quantified the light distributions
of each galaxy based on a combination of aperture photometry, Sérsic+PSF (point spread function) and multi-component decom-
positions, and non-parametric measures of morphology. From these analyses, we derived the galaxy colours, structural parameters,
non-parametric morphological indices (Concentration C; Asymmetry A, Clumpiness S ; Gini G; second order moment of light M20 ),
and structural complexity based on multi-component decompositions. These quantities were then compared between the Fornax main
cluster and Fornax A group. The structural complexity of Fornax galaxies were also compared to those in Virgo and in the field.
Results. We find significant (Kolmogorov-Smirnov test p-value < α = 0.05) differences in the distributions of quantities derived from
Sérsic profiles (g0 − r0 , r0 − i0 , Re , and µ̄e,r0 ), and non-parametric indices (A and S ) between the Fornax main cluster and Fornax A
group. Fornax A group galaxies are typically bluer, smaller, brighter, and more asymmetric and clumpy. Moreover, we find significant
cluster-centric trends with r0 − i0 , Re , and µ̄e,r0 , as well as A, S , G, and M20 for galaxies in the Fornax main cluster. This implies that
galaxies falling towards the centre of the Fornax main cluster become fainter, more extended, and generally smoother in their light
distribution. Conversely, we do not find significant group-centric trends for Fornax A group galaxies. We find the structural complexity
of galaxies (in terms of the number of components required to fit a galaxy) to increase as a function of the absolute r0 -band magnitude
(and stellar mass), with the largest change occurring between -14 mag . Mr0 . −19 mag (7.5 . log10 (M∗ /M ) . 9.7). This same
trend was found in galaxy samples from the Virgo cluster and in the field, which suggests that the formation or maintenance of
morphological structures (e.g. bulges, bar) are largely due to the stellar mass of the galaxies, rather than the environment they reside
in.
Key words. galaxies: clusters: individual: Fornax – galaxies: groups: individual: Fornax A – galaxies: interactions – galaxies:
evolution – galaxies: structure – galaxies: photometry
1. Introduction tion through cosmic time to the present day. The large variety
of structures present in galaxies (e.g. bulges, bars, spirals) has
The shapes and sizes of galaxies we observe today are a product led to sophisticated classification schemes (e.g. Hubble 1936; de
of their initial conditions in the early Universe and their evolu- Vaucouleurs 1959; Sandage 1961; Buta et al. 2015), which qual-
Article number, page 1 of 36
A&A proofs: manuscript no. aanda
itatively describe the light distribution of galaxies. However, a enough to create a dichotomy in colour-magnitude diagrams,
qualitative description alone does not provide enough informa- with the gas-poor early-type galaxies (ETG) predominantly re-
tion on how certain mechanisms and physical processes affect siding along the red sequence (RS), and the blue cloud which
structures; a quantitative description is required. consists of gas-rich late-type galaxies (LTG).
One method to quantify the structure of galaxies is through Although the potential environmental mechanisms for trans-
photometric decomposition, where the light distribution of forming galaxies from the blue cloud to the red sequence are
galaxies is broken into individual components. This method has known (e.g. Gómez et al. 2003), it is not clear at which stage
been widely used in the literature as it allows for a system- of group and cluster evolution this occurs. Due to the differ-
atic analysis of the structures (e.g. bulges, disks, bars) within ences in mass between clusters and groups, the efficiencies of
galaxies. A wide range of tools have been developed to im- the aforementioned mechanisms vary. For example, the lower
plement 2D photometric decompositions (e.g. GIM2D, Simard velocity dispersion in groups allows for more prolonged inter-
1998; GALFIT, Peng et al. 2002; BDBAR, Laurikainen et al. actions between galaxies. This can lead to more efficient tidal
2004; BUDDA, de Souza et al. 2004; GASP2D, Méndez-Abreu stripping, causing galaxies to become more asymmetric. Such
et al. 2008; IMFIT, Erwin 2015). A number of studies have con- changes in the morphology of galaxies is known as preprocess-
ducted photometric decomposition on large galaxy samples (e.g. ing (Zabludoff & Mulchaey 1998; Fujita 2004). With different
Allen et al. 2006; Simard et al. 2011; Lackner & Gunn 2012), mechanisms becoming more significant depending on environ-
although in some cases only one- or two-component models ment, the signs of preprocessing and the cluster environment’s
are fitted. Single Sérsic or Sérsic and nucleus decompositions impact on the evolution of galaxies should be encoded in the
can provide a useful global characterisation of the galaxy light galaxies’ structures and stellar populations.
distribution, particularly for low mass galaxies (e.g. Graham & The Fornax cluster is a great laboratory for studying the im-
Guzmán 2003). When multiple (i.e. three or more) components pact of galaxy environments, as it consists of both a main cluster
are fitted simultaneously, such as for a galaxy hosting a bulge, a and an infalling group. The main cluster is centred on NGC 1399
disk, and a bar, the number of free parameters makes the decom- (R.A.= 03h38m29.1s, Dec.= −35d27m02s, Kim et al. 2013) and
position model heavily degenerate and the resulting solution, if located at a distance of 20.0 ± 0.3 ± 1.4 Mpc (Blakeslee et al.
the fitting procedure converges to a solution at all, easily be- 2009). Fornax is a relatively low mass cluster ((7 ± 2) × 1013 M ,
comes uninformative. However, with reasonable, physical initial Drinkwater et al. 2001) compared to the most nearby galaxy
conditions for each component as well as human supervision of cluster, Virgo ((4.2 ± 0.5) × 1014 M , McLaughlin 1999). De-
the fitting procedure, multi-component decompositions are fea- spite its relatively low mass, Fornax appears to be at a more
sible and can yield insight into galaxy formation scenarios (e.g. advanced evolutionay state than Virgo. For example, the For-
Graham 2002; Laurikainen et al. 2007, 2018; Erwin et al. 2008; nax core appears more dynamically relaxed and is dominated by
Gadotti 2009; Salo et al. 2015; Méndez-Abreu et al. 2017; Kruk quenched ETGs (Ferguson 1989). Despite this, the infalling For-
et al. 2018; Spavone et al. 2020). nax A group, demonstrates the ongoing assembly of the Fornax
For massive galaxies (e.g. M∗ & 109 M ), multi-component cluster.
decompositions are important in obtaining accurate parameters As one of the latest wide-field observations of the Fornax
of structures. For example, several works (e.g. Laurikainen et al. cluster, the Fornax Deep Survey (FDS) provides an opportunity
2004, 2010; Aguerri et al. 2005; Gadotti 2008) have shown that to probe the cluster in outstanding detail. With FDS, Iodice et al.
if the bar is not accounted for in the decomposition model, the (2019) studied the surface brightness profiles of bright (mB ≤
bar flux can be erroneously attributed to the bulge flux. This was 15 mag) ETGs within the inner 9 square degrees (< 700 kpc) of
confirmed for a larger sample of galaxies in Salo et al. (2015). It the Fornax cluster. They found that there is a high density of
has also been shown that in a nearly face-on view the vertically ETGs and intracluster light (ICL) in the western region of the
thick inner bar component is often erroneously attributed to the cluster (∼ 0.3 Mpc from the core) and that many of the ETGs
bulge flux (see Laurikainen et al. 2014; Athanassoula et al. 2015; show signs of asymmetry in their outskirts. This was interpreted
Salo & Laurikainen 2017). as evidence for a group accretion event during the formation
It is known that the evolution of galaxies is dependent on of the cluster, and also suggests that the core is still virialis-
internal processes, which are correlated with the stellar mass ing. More recently, Spavone et al. (2020) found the luminosity
(Kauffmann et al. 2003; Haines et al. 2007), and external pro- of the ICL within the virial radius of the Fornax cluster to be
cesses due to the environment (Dressler 1980; Jaffé et al. 2016). ∼ 34% of all cluster members. Additionally, the Next Genera-
Considerable work has also been done to address the envi- tion Fornax Survey (NGFS), another deep, wide-field survey of
ronmental dependence of galaxy evolution (e.g. star formation the Fornax region, has also studied the dwarf galaxies popula-
quenching) throughout cosmic history (Baldry et al. 2006; Peng tion, in terms of structural parameters (Eigenthaler et al. 2018)
et al. 2010b; Nantais et al. 2016, 2017, 2020; Old et al. 2020). and sub-structures in their spatial distribution (Ordenes-Briceño
Several mechanisms have been proposed by which the environ- et al. 2018). They found that nucleated galaxies tend to be larger,
ment can change the morphology of galaxies, such as mergers brighter, and tend to be concentrated towards NGC 1399 than
(Barnes & Hernquist 1992), high speed close encounters with non-nucleated galaxies, which agree with the results of Venhola
neighbouring galaxies combined with tidal interactions with the et al. (2019).
cluster potential (i.e. harassment, Moore et al. 1998; Smith et al. Venhola et al. (2018) presented the FDS Dwarf Catalogue
2015), the removal of cool gas via ram pressure stripping (Gunn (FDSDC), a catalogue comprised of 564 likely cluster mem-
& Gott 1972; Boselli & Gavazzi 2014), or halting of gas accre- ber dwarf galaxies reaching a 50% completeness limit at Mr0 =
tion (i.e. strangulation or starvation, Larson et al. 1980). These −10.5 mag and µ̄e,r0 = 26 mag arcsec−2 . The catalogue spans an
processes can also affect the star formation of the galaxies, such r0 -band absolute magnitude of −9 mag to −18.5 mag. Using this
as merging galaxies often hosting signs of strong star formation catalogue, Venhola et al. (2019) found that dwarf galaxies tend
at certain merger stages (e.g. Sanders et al. 1988; Di Matteo et al. to become redder, smoother, and more extended with decreasing
2008), or the removal of gas making galaxies become quiescent distance to the cluster centre. The fraction of early- and late-type
(Bekki et al. 2002). The change in star formation can be large dwarfs was observed to vary as a function of cluster-centric dis-
Article number, page 2 of 36
A. H. Su et al.: The Fornax Deep Survey (FDS) with the VST
tance. Additionally, they found that early- and late-type dwarfs
follow different scaling relations in absolute magnitude, effective
< Rvirial
radius, and Sérsic index. The observations are consistent with the 33 > Rvirial
idea that the star formation in dwarf galaxies is quenched via ram Fornax group
NGC1399
pressure stripping. Only the most massive dwarfs can manage to 34 NGC1316
Dec. (J2000) [deg]
hold on to some cold gas in their cores.
Taking advantage of the deep imaging of FDS, we aim to 35
study the signs of preprocessing between two different environ-
ments: the Fornax main cluster and Fornax A group (for brevity, 36
we refer to the main Fornax cluster as ’Fornax main’, and the
Fornax A group as ’Fornax group’). Specifically, we compare the 37
galaxies between the two environments to see if environmental
processes can explain any of the observed differences, and dis- 38
cuss which of the processes are driving such differences. In order 58 57 56 55 54 53 52 51 50 49
to compare the two environments, we accurately measure and RA (J2000) [deg]
quantify the structures in the galaxies. Several works demon-
strate a first look at this, using azimuthally averaged multi-
component decompositions to quantify accreted vs. in-situ com- Fig. 1. Positions of our sample of Fornax main member galaxies. The
ponent of the brightest Fornax galaxies (Iodice et al. 2019; Raj sample was split between the Fornax main cluster (blue and purple) and
et al. 2019; Spavone et al. 2020; Raj et al. 2020). To this end, the Fornax group (orange) based on the separation relative to NGC 1399
one of the main focuses in this work is the 2D multi-component and NGC 1316. Using NGC 1399 and NGC 1316 as the centres of the
Fornax main and Fornax group, respectively, the projected separation
decompositions of galaxies in the FDS for both dwarfs and gi-
between the two centres is ∼ 3.6 ◦ . Galaxies which have a projected
ants. Not only do such decompositions provide information on separation to NGC 1399 that is greater than twice the projected separa-
the structural complexity of the galaxies, but also provide oppor- tion to NGC 1316 were classified as belonging to the Fornax group. The
tunities to study the structures themselves. Furthermore, as we dashed blue and red lines denote 2 ◦ (= 700 kpc) and 1 ◦ from NGC 1399
follow the same decomposition procedures used in the Spitzer and NGC 1316, respectively.
Survey of Stellar Structure in Galaxies (S4 G Salo et al. 2015;
Sheth et al. 2010), this work serves to expand the catalogue of
multi-component decompositions to even lower stellar masses. which covers a field of view of 1 ◦ × 1 ◦ and the pixel size is
To complement the multi-component decompositions, we also 0.21 arcsec pix−1 . Observations were conducted in u0 , g0 , r0 , and
present the Sérsic+PSF models and non-parametric measures i0 bands under average seeing, with full width at half maximum
(see Sect. 4.3 for definitions) of member galaxies of the Fornax (FWHM) of 1.2 00 , 1.1 00 , 1.0 00 , and 1.0 00 , respectively (Venhola
main and the Fornax group. et al. 2018). For more details on the observing strategy, see
This paper is structured in the following manner. In Sect. 2 Iodice et al. (2016) and Venhola et al. (2018). The steps for the
we discuss the FDS data and the sample selection. Sect. 3 out- reduction and calibration of FDS mosaics covering 26 deg2 can
lines the steps taken to prepare the data for analysis. In Sect. 4 be found in Venhola et al. (2018). The final mosaics were re-
we outline the process of measuring aperture photometry, con- sampled to have a pixel scale of 0.20 arcsec pix−1 . Given that the
ducting structural decompositions, and the calculation of non- FDS coverage of the u0 -band is limited to the main cluster, we
parametric indices. In Sect. 5 we compare the magnitudes de- exclude u0 -band data from our analyses.
rived from photometry and with FDSDC, as well as calculate the
galaxy stellar masses. In Sect. 6 we present the galaxy properties
as a function of stellar mass and projected cluster-/group-centric 2.2. The sample
distance (hereon referred to as halo-centric distance), and com- The sample of galaxies used in this work is based on the cata-
pare the differences between Fornax main cluster and Fornax logue of all FDS sources and cluster membership selection pre-
group with stellar mass trends removed. In Sect. 7 we compare sented in Venhola et al. (2018). This consists of 594 galaxies, of
our results with similar studies in the literature and in Sect. 8 we which 564 overlap with FDSDC. The 30 additional galaxies not
discuss the potential mechanisms which led to the observed dif- within FDSDC contain 29 bright (Mr0 < −18.5) galaxies, which
ferences between these environments. Finally, in Sect. 9 we sum- include the samples from Iodice et al. (2019), Raj et al. (2019,
marise and draw our conclusions for this work. Throughout this 2020), and one dwarf galaxy missing from FDSDC. From visual
work a distance modulus of 31.5 mag (equivalent to a distance inspection of the images, we removed 8 FDSDC entries from
of 20 Mpc) (Blakeslee et al. 2009) was used. At this distance, our sample due to duplication (see Appendix A for more detail).
1 arcsec corresponds to ∼ 0.097 kpc. Due to the lack of i0 -band coverage in the FDS33 field1 , the 4
galaxies2 located in this field were excluded from the final sam-
ple, although decompositions were made in g0 and r0 . Overall,
2. Data our final sample consists of 582 galaxies. Figure 1 shows the po-
2.1. Observations sitions of the galaxies and visualises how we define the Fornax
main and Fornax group sub-samples.
This work uses data from the Fornax Deep Survey (FDS), which In terms of the assignment of galaxy IDs, we adapted the
is a joint collaboration between two guaranteed time obser- naming convention used in FDSDC to include both dwarfs and
vation surveys: FOCUS (PI: R. Peletier) and VEGAS (PI: E. massive galaxies. Our format is FDSX_YYYYz, where the pre-
Iodice, Capaccioli et al. 2015). FDS covers the Fornax main
cluster and the infalling Fornax group using the 2.6 m ESO VLT 1
For an overview of FDS fields, see Venhola et al. (2018), their Fig. 2
Survey Telescope (VST), located at Cerro Paranal, Chile. Imag- 2
FDS33 galaxies: FDS33_0081, FDS33_0088, FDS33_0121,
ing was completed using OmegaCAM (Kuijken et al. 2002), FDS33_0129
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A&A proofs: manuscript no. aanda
fix "FDS" is always present, X denotes the FDS field number image centred on the brightest pixel. The centroid is calculated
(which can be up to two digits), Y denotes the identification as the coordinate where the change in pixel intensity with x and y
number within the field and always contains four digits (filled become zero. This process is iterated six times, where each time
with zeroes), and in some cases (where nearby companions were the output coordinate is used as the new input coordinate, but
detected during the catalogue creation of FDSDC Venhola et al. with an increasingly lower radius. This maximises the chances
2018) z is included as one letter as part of its identification of convergence to determine the brightest pixel, and allows for
number. We note that the majority of galaxies not contained a larger margin of error in the initial guess. Nevertheless, the
in FDSDC already had identification numbers assigned during centroid routine may fail in some cases when the light distribu-
the catalogue creation process. For three galaxies3 which lacked tion has no clear central peak (i.e. a flat light profile). In such
identification numbers, their identifications were assigned as one cases, the central coordinates were selected by cursor after de-
plus the highest identification number within the FDS field (i.e. tailed inspection of the galaxy images based on both geometric
for FDS7 field the last identification number was 735, so the two considerations and the brightest pixel position. These typically
galaxies not included in FDSDC were labelled as 736 and 737). agreed with those from FDSDC, which used isophotal coordi-
This naming convention is used in the table of multi-component nates5 from SExtractor for flat galaxies (Venhola et al. 2018).
decompositions in Appendix I.1 as well as on the webpage4
where all the decompositions are presented (see Appendix D).
3.2. Sky subtraction
Although the FDS mosaics were first-order background sub-
3. Data processing
tracted during the data reduction process, the local level of sky
To prepare the data for decompositions, we followed the proce- background must be taken into account for each galaxy. We esti-
dures used in the S4 G decomposition pipeline (Salo et al. 2015). mate the sky levels through three different steps. First, we placed
The processing steps include determining the central pixel of square boxes with widths of 12 arcsec (hereafter: skyboxes) via
each galaxy, creating masks with SExtractor (Bertin & Arnouts cursor around the masked postage stamp image. The locations of
1996) which were modified (if necessary) after visual inspec- the skyboxes were chosen as regions devoid of obvious sources,
tion, determining average sky values, and using IRAF ellipse external to the galaxy. On average 12 skyboxes were placed for
to obtain isophotal position angle and ellipticity profiles. From each galaxy. For each skybox, the median and the standard devi-
this, initial guesses for decomposition parameters can be esti- ation of the flux values enclosed (excluding masked pixels) were
mated. This reduces possible degeneracies in the model and the calculated. The sky level was then estimated as the mean of the
probability of χ2 minimisation becoming ’stuck’ in erroneous lo- median values and the root mean square (RMS) of the sky level
cal minima. Appendix C.1 illustrates the various steps taken in as the mean of the standard deviation values. From this initial sky
the data processing procedure. level estimate, we create a sky subtracted image and produce an
ellipticity and position angle (PA) profile as a function of radius
from the centre, via the IRAF ellipse routine. Through inspec-
3.1. Image preparation tion of the sky subtracted image and the ellipticity and position
First, the postage stamp images of each galaxy were cut out from angle profiles we calculate the mean ellipticity and position an-
the FDS mosaics reduced by Venhola et al. (2018). The postage gle for the galaxy outskirts (see Fig. C.2).
stamp images cover at least five effective radii (from Venhola Fixing the ellipticity and position angle to the mean values
et al. 2018), with a lower limit of 100 arcsec (= 500 pix) in both calculated, we construct elliptical annuli of increasing radii upon
dimensions. the postage stamp image. The width of the annuli was defined
Before creating the mask images, IRAF (Tody 1986) to increase by 2% logarithmically (with a minimum width of
ellipse and bmodels were used to provide model images of 1 pix) to increase the signal-to-noise ratio (S/N) in the galaxy
the galaxies. Residual images were constructed by subtracting outskirts. For each annulus we applied 3σ clipping of pixel in-
the model images from the galaxy images, which were used as tensities (where σ is the RMS value within the annulus). Pixels
input for SExtractor. By using the residual images instead of the which were rejected or masked away were replaced with the av-
galaxy images directly, the binary segmentation maps cover any erage pixel value within the annulus plus Gaussian RMS noise.
unwanted sources (e.g. stars) overlapping the galaxies. The bi- Through this process, a ’cleaned’ image of the galaxy is created,
nary segmentation maps were convolved with a Gaussian kernel hereon referred to as cleanimage.
with σ = 3 pix in order to extend the size of the resultant masked From the cleanimage, an azimuthally averaged flux profile
areas. The convolved segmentation maps were then converted and a cumulative flux profile were constructed for each galaxy.
back to binary mask images by using a threshold in the pixel The profiles allowed for clear visual inspection of the radius at
values (in this case 0.03). Each mask image was inspected by which the galaxy flux is no longer significant compared to the
eye and manually edited where necessary (e.g. when structures background noise (we refer to this radius hereon as radgal, see
belonging to the galaxy are erroneously masked, or those with Fig. C.3). Beyond this conservative maximum galaxy radius, we
close bright companions). chose an additional range in radius to create a sky annulus in the
The central pixel coordinates of the galaxies were deter- cleanimage. The second estimate of sky level and RMS value
mined using a centroid fitting routine which finds the brightest were calculated as the mean and standard deviation of the pixel
pixel within a threshold radius from an input x and y coordinate. values within this sky annulus, respectively.
The initial guesses were chosen via a cursor on the galaxy im- By default, a flat sky subtraction based on the sky level from
ages, which were typically chosen to be near the brightest pixels. the second approach was applied to the postage stamp images
The routine then computes the centroid based on a section of the
5
SExtractor galaxy coordinates were calculated as the first
3
FDS7_0736, FDS7_0737, FDS31_0607 order moment of the detection image, according to SExtrac-
4
https://www.oulu.fi/astronomy/FDS_DECOMP/main/index. tor manual https://sextractor.readthedocs.io/en/latest/
html Position.html#pos-iso-def.
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A. H. Su et al.: The Fornax Deep Survey (FDS) with the VST
to create the data images used in GALFIT. In select cases (37 have pixel values calculated based on the standard deviation of
in total) where a strong gradient in the sky was observed (e.g. the background noise in each frame and the number of over-
galaxies close to bright foreground stars) we also fit and sub- lapping frames for each pixel. In the weight images bad pixels
tracted a plane from the background sky, as in such cases we were assigned a value of zero. The photon counts can be thought
found that a flat sky subtraction significantly biased the galaxy’s to follow a Poissonian distribution. Combining both sources of
measured total magnitude. After the sky subtraction, the elliptic- noise, the corresponding sigma values for each pixel are defined
ity and PA profiles were reiterated with the data images to recal- as
culate the mean ellipticity and PA of the outer isophotes. These
values were taken as initial values for the decompositions. !2 s
0.20 1 f
sigma = + , (1)
0.21 W g
3.3. Point spread function
where W is the weight image, f is the flux value from sky
In order to obtain accurate parameters in decompositions, the subtracted science image (i.e. data image), g is the conversion
point spread function (PSF) must be taken into account. In prin- value between analog to digital units (ADUs) and electrons, also
ciple there are variations in the PSF across different observa- known as the gain (read from image headers), and the factor
tions, so deriving the PSF on a galaxy-by-galaxy basis can be of (0.20/0.21)2 accounts for the resampling of the science and
more accurate. However, there must be enough suitable stars weight images during calibration, changing from the instrumen-
close to the galaxy, else the uncertainty in these local PSFs tal pixel scale of 0.21 arcsec to the final image pixel scale of
will be skewed by the small sample sizes. As a result, we pro- 0.2 arcsec. The sigma images were checked via inspection to en-
duced a separate PSF for each FDS field instead to apply to sure that their pixel values calculated in the sky region corre-
the galaxies residing in the corresponding field. To sample the spond with the measured sky RMS values.
PSFs we first use SExtractor to build a catalogue of objects in
each FDS field. From the catalogue, a set of selection crite- 4. Photometric parameters
ria was applied to select only point sources. Upper and lower
limits were placed on the magnitude of the objects (typically Several techniques have been developed to extract information
20 > MAG AUT O > 15.5 across the fields) to ensure that they about objects from images. Given that different methods have
are neither saturated nor have too low S/N. Then, to exclude ex- distinct advantages and disadvantages, we employ a number of
tended objects, an upper limit on the parameter FWHMI MAGE techniques to explore our data. We apply aperture photometry to
was placed which varied from field to field (typically ∼ 1 arcsec). measure the distribution of light and calculate parameters (e.g.
Furthermore, 1/ELONGAT ION > 0.95 was used to exclude integrated magnitudes, effective radii). We also employ photo-
elongated objects, such as inclined galaxies. In addition, the metric decompositions to quantify the light distribution of phys-
quality flag produced in the catalogue was used so that objects ically motivated components (e.g. bulge, disks, bars etc.). Addi-
with FLAG ≥ 1 were excluded. From the selection cuts, a sam- tionally, we calculate non-parametric morphological indices in
ple of point sources was made which typically consisted of a few order to characterise our galaxies without explicitly imposing
hundred objects. any model assumptions upon them.
From the sample of point sources, we first normalised the
postage stamp images by its total flux (based on MAG AUT O ). 4.1. Aperture photometry
Next, a 2D Gaussian was fitted to the image (101 × 101 pix)
of each source in order to determine the central peak with sub- In order to measure the light distribution of the galaxies, we
pixel accuracy. Using the new centres, radial flux profiles for utilised the azimuthally averaged and cumulative flux profiles
all sources were made and ’stacked’ to create a general pro- made from cleanimages, as defined in Sect. 3.2. To summarise,
file for each field. The profile was median averaged in bins of the cleanimages were constructed via iterative 3σ clipping of
0.05 arcsec within the inner region (A&A proofs: manuscript no. aanda
quantities in the g0 and i0 band using the limiting galaxy radius. – The Sérsic function (sersic) has the form
In case the cumulative flux profile in g0 or i0 -band starts to de- !1/n
cline before the limiting radius, the maximum cumulative flux r
Σ(r) = Σe exp −bn − 1 ,
was taken instead. (4)
re
where r is the isophotal radius, re is the effective radius (i.e.
4.2. Structural decompositions radius which contains half the total flux), Σe is the surface
Photometric decomposition of galaxies involves fitting paramet- brightness at effective radius (in sky-plane), n is the Sérsic
ric functions to their light profiles (e.g. the de Vaucouleurs’ pro- index, and bn is the normalisation factor dependent on the
file for elliptical galaxies, de Vaucouleurs 1948). By calculating Sérsic index.
parameters which best fit a galaxy, one can characterise the fea- – The exponential function (expdisk) has the form
tures of the galaxy with a simple set of values. This becomes r
!
particularly useful when structures are broken down into a lin- Σ(r) = Σ0 q−1 exp − , (5)
ear combination of different functions. By decomposing a galaxy rs
into components, not only can one study the different structures where r s is the scale length (i.e. radius where the peak flux
individually but also relative to the rest of the galaxy. has fallen by 1/e), Σ0 is the central surface brightness (face-
on), and q is the axial ratio (= b/a). The combination of
4.2.1. GALFIT Σ0 q−1 is equivalent to the central surface brightness in the
sky-plane.
For the galaxy decomposition we utilise GALFIDL (Salo et al. – The edge-on disk function (edgedisk) has the form
2015), an IDL interface which allows for batch processing of " # ! !
galaxies with GALFIT, ver. 3 (Peng et al. 2010a) and visualisa- r r h
Σ(r, h) = Σ0 K1 sech2 , (6)
tion of the output decomposition models. GALFIT is a galaxy rs rs hs
fitting tool which fits parametric functions to 2D light profiles of
galaxies and outputs the best fitting parameters. Minimisation is where r s is the scale radius, h s is the scale height, Σ0 is the
done using the Levenberg-Marquadt algorithm, with the good- central surface brightness (face-on, same as in exponential
ness of fit χ2 defined as function), and K1 is the modified Bessel function. The func-
tion is derived from van der Kruit & Searle (1981) (see their
Eqn. 5), which has an exponential radial dependence. There-
X X O(x, y) − M(x, y)2 fore here Σ0 is the same as denoted in the expdisk function.
χ2 = , (3)
x y
σ(x, y)2 – The Ferrers function (ferrer) has the form
!2−β α
r
Σ(r) = Σ0 1 − ,
where O is the observation (data image), M is the model image
(7)
rout
(parametric function convolved with PSF), σ is the uncertainty
in the observation (sigma image), and x and y denote the pixel where rout is the outer truncation radius, Σ0 is the central
index in the x and y axes of the images, respectively. surface brightness (in sky-plane), α is the parameter which
Additionally, GALFIT outputs the reduced χ2 , defined as dictates the gradient of the outer truncation, and β is the pa-
χν = χ2 /ν, where ν is the number of degrees of freedom in the
2
rameter which controls the gradient of the central slope. The
fit. In practice, ν is equivalent to the number of pixels used mi- Ferrers function is only evaluated within r < rout .
nus the number of free parameters in the model. χ2ν is a better – If a galaxy contains an unresolved element (e.g. a nucleus),
goodness-of-fit indicator than χ2 as the value is normalised to the the PSF is used to model this component.
size of the image. In the ideal case where the model matches the
data within the given uncertainties, the expected value of χ2ν = 1.
χ2ν < 1 implies that the uncertainties are likely to be overesti- 4.2.2. Decomposition strategy
mated, whereas χ2ν > 1 indicates a difference between the model We apply two main types of decomposition models for our
and the data (or underestimated uncertainties). In practice, χ2ν sample of galaxies: Sérsic+PSF and multi-component. The Sér-
typically exceeds 1, particularly for larger, more massive galax- sic+PSF models are useful as there is a significant number of
ies as the decomposition models do not completely account for galaxies that contain unresolved components within their light
the real structures in galaxies. For the same reason, the formal distributions (e.g. nucleus and a disk).
uncertainties calculated for the parameters are usually not very Such galaxies are not well described by a single Sérsic func-
useful; the actual uncertainties relate more to model selection. tion, as the unresolved component can cause misleadingly high
In GALFIT, the model images have isophotes in the form Sérsic indices. For Sérsic+PSF models, all Sérsic parameters
of a generalised ellipse (Athanassoula et al. 1990), which can aside from the centre coordinates were free to vary. For the PSF
be parameterised by the centre coordinates (xc , yc ), axial ratio function, which accounts for the unresolved nucleus, the same
(q = b/a, the ratio of semi-minor to semi-major axis), position centre coordinate as for the Sérsic function was used and the PSF
angle (PA, the orientation of the semi-major axis), and the shape magnitude parameter was allowed to vary up to a limiting mag-
parameter. The shape parameter C shape , which can adjust the nitude of 35. This constraint was applied to ensure that GALFIT
shape of the ellipse to be disky (C shape < 2) or boxy (C shape > 2), does not crash due to unreasonably faint PSF magnitude values,
was not modified in our decompositions (i.e. we assumed simple such as in cases where the galaxy does not contain a nucleus (the
elliptical shapes, C shape = 2, for all cases). In conjunction with model effectively turns to single-Sérsic). We note that the limit of
the ellipses, there are several radial parametric functions to fit 35 magnitudes is very conservative and in practice a PSF mag-
the 2D galaxy light distribution: nitude fainter than 30 can be regarded as non-nucleated, given
Article number, page 6 of 36A. H. Su et al.: The Fornax Deep Survey (FDS) with the VST
that the contribution of flux from such a component becomes 4.3. Non-parametric measures
negligible.
To further quantify our sample of galaxies, we also compute
In order to fit multiple components in galaxies, one must first
some non-parametric morphological measures for each galaxy.
choose a parametric function. In principle there is no limita-
From the literature, there are several non-parametric indices
tion on the choice of function for decompositions, but in prac-
which measure morphological features. The most popular in-
tice some limitations are useful. For example, dwarf galaxies
dices have been based on Conselice (2003) and Lotz et al.
are generally well described by Sérsic functions. However, for
(2004), although the exact definitions can vary from study to
more complex galaxies (i.e. with distinct morphological struc-
study. Therefore, here we introduce and define the specific pa-
tures, such as in Caon et al. 1994) we assign a specific set of
rameters we use in this work. All measures were calculated using
functions in GALFIT to fit the physical components in a galaxy.
elliptical apertures with position angles and ellipticities defined
This allows us to remain systematic in conducting decomposi-
in Sect. 3.2. When referring to radius we mean the semi-major
tions. The list of functions used are:
axis of the elliptical aperture.
– bulge: sersic,
– disk: expdisk, or edgedisk if edge-on, 4.3.1. Concentration (C )
– bar: ferrer2,
The concentration index, following Conselice (2003), is defined
– nucleus: psf, as
– barlens8 : expdisk.
C = 5 log10 (R80 /R20 ), (8)
For the multi-component decompositions, we employ the
philosophy of beginning with a simple model and gradually where R80 and R20 are the radii which enclose 80% and 20% of
building up complexity as required. As an initial step, visual the Petrosian flux, respectively. The Petrosian flux is determined
inspection of the data images provided conservative estimates as the total flux enclosed within 1.5 times the Petrosian radius
of the structures present within the galaxies. This identifies the (rpetro ), which in turn is defined as the radius where the flux is
most significant (in terms of flux contribution) structures and equal to 0.2 times the average flux within the same radius. The
components (e.g. bulges, disks) which allow for the majority concentration index provides insight into how much of the flux
of the galaxy’s flux to be modelled. Additionally, the model of a galaxy is distributed towards the centre. The more centrally
and residual images of the Sérsic+PSF decompositions were in- concentrated the flux, the higher the value. For a single Sérsic
spected for additional inner structures (or lack thereof). The el- model, n = 1 corresponds to C = 2.80, n = 2 to C = 3.80, and
lipticity and position angle profiles also occasionally suggested n = 4 to C = 5.27.
bars were present, based on an increase in ellipticity but rela-
tively constant position angle with increasing radii. 4.3.2. Asymmetry (A)
For all multi-component models all component centres were
fixed to the values determined in Sect. 3.1. Furthermore, the ax- The asymmetry index is defined as
ial ratio and position angle of the outermost component (i.e. the P !
|I0 − I180 |
component with the largest effective radius/scale length, typi- A = min P − Abackground , (9)
cally a disk component) were fixed to the outer isophote val- |I0 |
ues measured in Sect. 3.2. This reduced the degeneracy and where I0 and I180 are the original and 180◦ rotated galaxy im-
increased GALFIT’s fitting speed. After the initial decomposi- ages, respectively, Abackground is a correction term to account for
tions, we inspected the residuals and iterated the decomposition the contribution to A from the background noise and is defined
models accordingly. Due to degeneracies from simultaneously as
fitting multiple components, the output model parameters from
GALFIT which minimise χ2 are not always physically meaning-
P
|B0 − B180 |
Abackground = , (10)
ful. This can occur with the Sérsic n and the Ferrers α and β
P
|I0 |
parameters.
As an example, Fig. 2 shows an overview of the Sér- where similarly B0 and B180 are the original and 180◦ rotated
sic+PSF and multi-component decompositions for FDS25_0000 area of the background sky, respectively. The centre of rotation
(NGC 1326). In the surface brightness profiles we show the val- (i.e. asymmetry centre) is determined as the coordinate which
ues from the masked galaxy data image as well as the overall minimises the first term of Eqn. (9). Furthermore, only pixels
decomposition models. We add Gaussian noise to the decom- within 1.5 × rpetro are used in this minimisation term. We calcu-
position models (with standard deviation equal to the sky RMS lated Abackground using the same asymmetry centre for rotation,
measured in Sect. 3.2) so that visual comparison with the masked unlike what was defined in Conselice (2003) where Abackground
image is possible. The differences in surface brightness between was minimised separately. As the number of pixels in I0 and B0
the (masked) galaxy and the models are shown in the residual im- may differ for each galaxy, we multiply Abackground with a scale
ages, which in this case amounts to prominent galactic ring(s). factor based on the ratio of pixels in I0 and B0 (i.e. npix,I /npix,B ).
In Appendix I we list the type of multi-component models for The asymmetry index can range from 0–which implies a sym-
the 50 most massive galaxies (via stellar mass) in our sample. metric galaxy–to 2–which implies a highly asymmetric galaxy.
A full overview of the decompositions and tables of decompo-
sitions values can be found on our complementary website (see 4.3.3. Asymmetry profile
Appendix D).
To probe the outskirts of galaxies, particularly for potential signs
8
By barlens we mean the vertically extended central part of the bar of galaxy–galaxy interactions/tidal disruptions, we measure the
which, when viewed face-on, has a round appearance (see Laurikainen asymmetry parameter as a function of isophotal radius. The cen-
et al. 2014; Athanassoula et al. 2015; Salo & Laurikainen 2017). tre is fixed to the brightness centre (determined from Sect. 3.1)
Article number, page 7 of 36A&A proofs: manuscript no. aanda
Fig. 2. Overview of the Sérsic+PSF (upper row) and multi-component (lower row) decompositions for FDS25_0000 (NGC 1326). In the first
column from the left, we show the masked galaxy image (within the inner 150 arcsec region). The second column shows the surface brightness
profiles of the corresponding masked galaxy image as well as the decomposition models. The individual functions/components of the models
are also shown, which highlight their contributions to the overall model. The third and fourth columns show the model and residual images,
respectively, within the same region as the masked galaxy image. A range of 30 to 15 mag arcsec−2 was used to display the masked galaxy image,
the surface brightness profiles, and the model images. For the residual images a range of -1 to 1 mag arcsec−2 was used instead.
and A is calculated within elliptical annuli. The brightness cen- (2004)
tre was used instead of the asymmetry centre as the profile be- npix
comes more sensitive to asymmetric features in the galaxy out- 1 X
G= (2i − npix − 1)| fi |, (13)
skirts, rather than at the centre for which the overall asymmetry h| f |inpix (npix − 1) i
is minimised. We used ellipse semi-major axes ranging from 0
to 1.5rpetro in steps of 0.25rpetro where | fi | is the absolute flux value of pixel i, h| f |i is the mean of
the absolute flux values of the image region used, npix is the to-
tal number of pixels, and where the summation is evaluated over
4.3.4. Clumpiness (S ) pixels ranked in ascending order of absolute flux. To evaluate
The clumpiness (sometimes referred to as the smoothness) index G we create the Gini segmentation map, where first a smoothed
is defined following Rodriguez-Gomez et al. (2019): image was created by convolving the original galaxy image with
P a Gaussian kernel of σ = 0.2 × rpetro . From the smoothed image,
(I0 − Iσ ) the mean flux at rpetro was calculated and set as a threshold, so the
S = P − S background , (11) segmentation map consists of pixels with flux greater than this
I0
threshold. If all pixels have equal flux value, then G = 0. Con-
where I0 and Iσ are the original and convolved galaxy images, versely, if all the flux is concentrated to one pixel, G = 1. The
respectively, and S background is the clumpiness of the background Gini values for galaxies tend to correlate with C, as galaxies tend
region, defined as to be brightest at their centres. However, G does not depend on
P the spatial location of the pixels, meaning galaxies which have
(B0 − Bσ ) highly concentrated light that is not necessarily at the centre of
S background = P , (12)
I0 the galaxy can still have high G values.
where B0 and Bσ are the original and convolved regions of the
background sky, respectively. A higher S value means that the 4.3.6. M20
galaxy is clumpier (e.g. due to star formation). The convolved
images are created by using a Gaussian kernel with σ = 0.25 × The normalised second order moment of the brightest 20% of
rpetro on the original images. Again, a scale factor of npix,I /npix,B light, M20 , traces the spatial extent of the brightest regions in
must be applied to S background . Pixels within 1.5×rpetro are used to a galaxy. To begin, the total second order moment of light is
calculate S , although there are two caveats: i) the central 0.25 × defined as
npix npix
rpetro region of the galaxy is excluded when calculating S as it X X
can be highly concentrated, and ii) only positive residual pixel Mtot = Mi = fi (xi − xc )2 + (yi − yc )2 , (14)
values are used in calculating S , as the ’clumpy’ features should i i
be brighter than the convolved counterpart. This applies to the where Mi is the second order moment of pixel i, npix is the total
summation of the galaxy images and the background sky region number of pixels, fi is the flux value of pixel i, and xi , yi and xc ,
in Eqn. (11). yc denote the x and y coordinates of pixel i and the centre, re-
spectively. The centre (xc , yc ) is defined as the coordinate which
4.3.5. Gini (G) minimises Mtot . M20 is then defined as
Pnpix,20
The Gini coefficient measures the distribution of flux values Mi
M20 = log10 i ,
(15)
amongst the pixels. We define the Gini coefficient as in Lotz et al. Mtot
Article number, page 8 of 36A. H. Su et al.: The Fornax Deep Survey (FDS) with the VST
where npix,20 denotes the brightest 20% of pixels. In practice, the Mband, aper Mband, aper
summations are evaluated over pixels set by the Gini segmen- 10 15 20 10 15 20
tation map, which defines npix . Additionally, the pixels in the g0 mean= 0.018, = 0.042
summations are sorted by flux in descending order to ensure that 0 r0
4
npix,20 contains 20% of the total flux from npix . i0
5 3
mband, decomp
mean=0.006, = 0.021
mband
4.3.7. Colour difference 10 2
In addition to the integrated colours (see Sect. 4.1), we also com-
pare the radial colour distributions of the galaxies within ellip- 15 1
tical apertures. The radial changes in colour can provide insight mean= 0.013, = 0.039
into the star formation history within a galaxy, hence environ- 20 0
mental effects can potentially be imprinted. We define the colour
difference as 20 15 10 24 20 16 12 8
mband, aper mband, aper
∆colour = colour(1Re < r < 2Re ) − colour(r < 0.5Re ), (16)
where r is the isophotal radius, colour(1Re < r < 2Re ) denotes Fig. 3. Left panel: Total magnitudes calculated from multi-component
the colour (e.g. g0 − r0 ) based on the total fluxes calculated be- decompositions as a function of aperture magnitudes, in g0 (green),
tween one and two effective radii of the galaxy, and similarly r0 (red), and i0 -band (fuchsia). Right panel: Difference in magnitudes
colour(r < 0.5Re ) denotes the colour calculated within half an ∆mband (i.e. mband,decomp −mband,aper ) as a function of aperture magnitudes.
effective radius of the galaxy. We calculate the colours within The black lines denote the mean and ±RMS in bins of 1 mag. The anno-
elliptical annuli with position angles and ellipticities measured tations show the mean and standard deviation of the means of ∆mband .
For better visibility, each magnitude relation is offset by 5 mag (left)
as explained in Sect. 3.2. A positive ∆colour implies a bluer in-
and 2 mag (right) relative to each other, with g0 -band as the reference
ner region and redder outer region, and vice versa for negative relation.
∆colour.
Table 1. Uncertainties in g0 , r0 , and i0 magnitudes and stellar mass.
5. Comparison of parameters mband,aper RMSg0 RMSr0 RMSi0 M∗
log10 ( M ) RMS M∗
We compare the parameters from aperture photometry against 8.50 - 0.05 0.06 - -
those from structural decompositions, both from this work as 9.50 0.07 0.05 0.03 11.25 0.06
well as those from FDSDC. This allows us to estimate the un- 10.50 0.03 0.03 0.06 10.75 0.05
certainty on the magnitudes, stellar masses, and decomposi- 11.50 0.05 0.07 0.07 10.25 0.07
tion parameters. Figure. 3 shows the total magnitudes calcu- 12.50 0.07 0.08 0.09 9.75 0.10
lated in this work via aperture photometry (see Sect. 4.1) and 13.50 0.07 0.03 0.05 9.25 0.09
multi-component decompositions (see Sect. 4.2). The right panel 14.50 0.04 0.05 0.06 8.75 0.07
shows the difference between aperture and decomposition mag- 15.50 0.05 0.05 0.07 8.25 0.09
nitudes for each galaxy. Overall, the magnitudes agree quite 16.50 0.06 0.05 0.09 7.75 0.11
well in the three bands, with the largest scatter occurring at the 17.50 0.08 0.06 0.11 7.25 0.14
faintest magnitudes. The scatter at the faint magnitudes are likely 18.50 0.07 0.08 0.15 6.75 0.17
due to the lower S/N. The uncertainties in the magnitudes for 19.50 0.09 0.08 0.14 6.25 0.20
each band are tabulated in left of Table 1. 20.50 0.09 0.07 0.19 5.75 0.29
For further comparisons, stellar masses were calculated to 21.50 0.11 0.10 0.32 5.25 0.37
characterise the galaxies. To estimate the stellar mass of our 22.50 0.27 - - - -
sample galaxies we adopt the empirical relation between colours
and stellar mass to light ratio (M∗ /L) from Taylor et al. (2011), Notes. Left: The RMS of the difference in aperture and decomposi-
adapted for SDSS bands (i.e. Venhola et al. 2019, their Eqn. 2): tion magnitudes for a range of aperture magnitude bins (black lines in
Fig. 3). These can be used as the estimated uncertainties of the calcu-
M∗
! lated magnitudes. Right: The mean RMS for stellar mass within bins
log10 = 1.15 + 0.70(g0 − i0 ) − 0.4Mr0 + 0.4(r0 − i0 ) (17) of log10 (M∗ /M ), which estimates the uncertainty in stellar mass due
M to uncertainties in the total magnitudes (i.e. values from the left). The
RMS were calculated based on 1000 Monte-Carlo realisations of stellar
where M∗ is the stellar mass, M is the solar mass, Mr0 is the mass for each galaxy in our sample (see Eqn. 17, and the accompanying
absolute r0 -band magnitude, and g0 , r0 , i0 denote the total magni- text in Sect. 5).
tudes in their respective bands. The stellar mass estimates have a
reported uncertainty of 0.1 dex within 1σ accuracy using only
g0 and i0 bands (Taylor et al. 2011). However, galaxies with This was done by conducting 1000 Monte-Carlo simulations for
log10 (M∗ /M ) < 7.5 were not included in deriving Eqn. (17), the stellar mass for each galaxy in our sample, using the total
so the relation must be extrapolated for lower masses. Venhola magnitudes and corresponding uncertainties from the left of Ta-
et al. (2019) used an independent stellar mass estimate based on ble 1. We then calculate the RMS and mean stellar mass for each
Bell & de Jong (2001) to test the uncertainty in the low mass galaxy and use the average RMS within bins of stellar mass as
region. They reported that both methods gave consistent results the uncertainty for a given stellar mass. This is shown in the right
within ∼ 10% error. On top of the uncertainty due to differences of Table 1.
in the methods used, we also calculate the contribution to stel- Figure 4 also compares the stellar masses we derive from dif-
lar mass uncertainties from the uncertainty in the magnitudes. ferent magnitude estimates, plotted as a function of aperture r0 -
Article number, page 9 of 36A&A proofs: manuscript no. aanda
1.2 0.8
mr , Aper
0 Mr , Aper
0
1.0
20 15 10 10 15 20 0.8 0.4
Sérsic g0 r0
22 8
(g0 r0)
Aperture Sersic+PSF 0.6 0.0
20 Multi-comp FDSDC mean=0.044, = 0.059 0.4
6 0.2 0.4
18 0.0 This work
FDSDC mean= 0.023, = 0.044
16 0.2 0.8
5 6 7 8 9 10 11 5 6 7 8 9 10
log10(M*/M )
14 4 mean=0.043, = 0.060 log10(M * /M ) log10(M * /M )
M*
12 101
0.4
10 2 0.2
log10(n)
mean=0.019, = 0.060
Sérsic n
8 0.0
0 100 0.2
6
0.4 mean= 0.005, = 0.026
4 10 15 20 25 6 8 10 12
Mr , Aper log10(M*, Aper/M ) 5 6 7 8 9 10 11 5 6 7 8 9 10
0
log10(M * /M ) log10(M * /M )
0.6
Sérsic Re [arcsec]
102 0.4
0.2
log10(Re)
Fig. 4. Comparison of stellar mass calculated via different methods of
determining total magnitudes, as a function of r0 -band total magnitudes 0.0
(left) and stellar mass from aperture photometry (right). Stellar masses 101 0.2
for FDSDC were estimated by multiplying the fluxes in each band by a 0.4 mean=0.016, = 0.014
factor of two, as the aperture used in Venhola et al. (2018) only extended 0.6
5 6 7 8 9 10 11 5 6 7 8 9 10
to one effective radius. The black lines denote the mean and ±RMS in log10(M * /M ) log10(M * /M )
bins of 1 dex. The annotations show the mean and standard deviation of
the means of ∆M∗ , where ∆M∗ is defined as the difference in exponents
(i.e. log10 (M∗,Aper ) − log10 (M∗,other )). For better visibility, each relation
Fig. 5. Comparison of the r0 -band Sérsic-derived quantities from Sér-
is offset by 3 dex relative to each other, with "Aperture" (pink) as the
sic+PSF decompositions between those made in this work and in
reference relation on the left plot whilst "Multi-comp" (green) as the
FDSDC9 . FDSDC sample ranges from −9 > Mr0 > −18.5, correspond-
reference relation on the right plot.
ing to 105 M . M∗ . 109 M . Left panels: The relations of Sérsic-
derived quantities as a function of stellar mass. Right panels: The dif-
ference in Sérsic-derived quantities for each galaxy (where the samples
band magnitude. The stellar masses were calculated from magni- overlap) as a function of the parameters determined in this work. Here ∆
tudes obtained by Sérsic+PSF and multi-component decomposi- is defined as the values from this work minus the values from FDSDC.
tions, as well as from FDSDC. Overall, there is good agreement The black lines denote the mean and ±RMS in bins of 1 dex. The anno-
regardless of method. As with magnitude estimates, the scatter tations show the mean and standard deviation of the means of ∆.
in stellar masses is larger for the lower mass galaxies. The multi-
component stellar masses give the smallest mean difference with
the aperture values (∼ 0.02 dex). For log10 (M∗ /M ) > 8, the 6. Fornax main versus Fornax group
RMS of the mean ∆M∗,Multi values is 0.01. Henceforth, we re-
fer to the stellar mass of galaxies as the values calculated via In order to investigate potential effects of the cluster environment
Eqn. (17) using magnitudes from multi-component decomposi- compared to the group environment, we split our galaxy sample
tions. into sub-samples (see Fig. 1): the Fornax main galaxies (cen-
In Fig. 5 we compare the quantities derived from the Sér- tred on NGC 1399/FDS11_0003) and the Fornax group galaxies
sic+PSF models (g0 − r0 , Sérsic n, and Re ) made in this work (centred on NGC 1316/FDS26_0001). From Drinkwater et al.
with those from FDSDC. The Sérsic n and Re were calculated (2001) the main cluster has a virial radius of 2 deg, whereas the
based on Sérsic+PSF decompositions on r0 -band images. For Fornax group does not have a well defined virial radius as it
the g0 − r0 colour, the g0 -band magnitudes were calculated us- is in the act of falling into the main cluster. Nevertheless, we
ing Sérsic+PSF models calculated from r0 -band (i.e. model with can estimate the group’s size using a 2σ limit in number density
n, Re , q, and PA fixed to values found from r0 -band decompo- (Drinkwater et al. 2001), which covers a region of ∼1 deg in ra-
sition for the Sérsic component, but the magnitude from Sérsic dius (see also Venhola et al. 2019, Fig. 3). Using these regions,
and PSF were free parameters to be fitted). For brevity, we de- we assigned to the Fornax group sub-sample all galaxies with
note these quantities derived from the Sérsic component of Sér- an angular separation to NGC 1399 that is greater than twice
sic+PSF models as ’Sérsic-derived quantities’. Regarding g0 − r0 the angular separation compared to NGC 1316. We exclude the
colours, FDSDC reports a redder colour for a handful of galax- two aforementioned galaxies with anomalous Sérsic n from fur-
ies at the low mass range (5.5 < log10 (M∗ /M ) < 6.5), but is ther analyses of Sérsic-derived quantities. The sub-sample sizes
otherwise consistent with our colours. Values of Sérsic n also for Fornax main and Fornax group are 497 and 83 galaxies, re-
agree quite well. We note, however, that some galaxies (in both spectively. Additionally, we utilise the ETG/LTG classification
studies) show n < 0.5. When converting to 3D, this would imply scheme of Venhola et al. (2018) for the dwarfs and extend it to
that there is a depression or ’dent’ in the light distribution of the the bright galaxies via visual inspections.
galaxy. In our cases it is more likely due to low S/N. In general
the distributions of Sérsic-derived quantities agree between the
FDSDC. For FDS10_0143 this is likely due to what we think is a fore-
two measures, with remarkably little scatter in Re . ground star overlapping with the galaxy, which the PSF component does
not fully account for. For FDS5_0010 the cause for the abnormally low
9
FDS10_0143 and FDS5_0010 fall outside the Sérsic n and ∆n Sérsic n is uncertain, although its r0 -band surface brightness profile does
plots, respectively, due to extreme values from this work compared to appear remarkably flat.
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